Open AccessLow light image enhancement based on non‐uniform illumination prior model
Author(s) -
Wu Yahong,
Zheng Jieying,
Song Wanru,
Liu Feng
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.6208
Subject(s) - naturalness , artificial intelligence , computer vision , hue , computer science , visibility , hsl and hsv , channel (broadcasting) , image (mathematics) , visualization , image restoration , enhanced data rates for gsm evolution , image enhancement , image processing , optics , physics , computer network , virus , quantum mechanics , virology , biology
Images captured under low‐light conditions are often of low visibility. To improve visualisation, a novel low light image enhancement method is presented based on the non‐uniform illumination prior model. First, the k ‐means method is used to process the value channel in the hue‐saturation‐value (HSV) colour space after space conversion of the input image. Then, the initial illumination of segmented scenes is estimated by an improved maximum red–green–blue method. Next, an illumination preservation method is presented to maintain the naturalness of the enhanced image. Furthermore, the non‐uniform illumination prior model is proposed to enhance the textural details in the enhanced image. Fast Fourier transformation is used to accelerate the optimisation. Since an adaptive weight is assigned, the proposed method can preserve the edges and textures at the bright and edge areas. Experimental analysis shows that the results using the proposed method have less noise, better illumination, improved contrast, and satisfactory naturalness. In addition, the proposed method can provide better quality images in terms of subjective and objective assessments.